How I Think
Engineering isn't just about writing code. These articles explain my approach to designing systems, making trade-offs, and solving problems.
How I Design ML Pipelines
My approach to building reproducible, maintainable machine learning workflows.
How I Handle Data Quality
Strategies for dealing with missing values, outliers, and data inconsistencies.
How I Approach Feature Engineering
The art and science of creating features that improve model performance.
How I Evaluate Models
Beyond accuracy: choosing the right metrics and validation strategies.
How I Organize ML Projects
Project structure, documentation, and reproducibility practices.
How I Debug ML Systems
Systematic approaches to finding and fixing issues in machine learning code.
My Engineering Philosophy
Start Simple
Begin with the simplest solution that could work. Complexity is easy to add, hard to remove.
Measure Everything
Intuition is valuable, but data is invaluable. Every decision should be backed by measurement.
Document Decisions
Code shows what, documentation shows why. Future-you will thank present-you.